Data gloves have been extensively studied in the past decade in the robotics and virtual reality communities. There are many ways of providing force feedback to a human from a virtual environment or from sensors on a robot, and many data gloves now make use of such force feedback. However, few data gloves collect touch-force data from the human fingers as the human interacts with the environment. To measure the forces acting at the fingers, for example, sensor pads comprising conductive rubber, capacitive sensors, and/or optical detectors can be placed between the fingers and the environment surface. These sensor pads, however, inevitably deteriorate the human haptic sense since the fingers cannot directly touch the environment surface. Moreover, sensor pads may deteriorate or wear out due to mechanical contacts.
Data glove based input has also been used increasingly in the last decade for teleoperation and other forms of human-machine interaction. Postural gesture recognition has been applied to the teleoperation of robots and the teaching of robots by demonstration and guiding. Some systems have been developed for teaching and guiding by inferring human intentions by tactile gestures, which were measured by force sensors on the robots. However, such a system is not very flexible, as it requires modification of the hardware on the robot.